265 results about "Bayesian neural networks" patented technology

The invention provides a chip production logistics optimization scheduling method and system based on deep reinforcement learning, and relates to the technical field of deep reinforcement learning. An improved bidirectional long-short-term memory network and a graph neural network are used for extracting a material flow time sequence rule, equipment operation cycle characteristics and logistics network topology characteristics, a fusion state representation is generated through a contrast learning network, a layered and collaborative deep reinforcement learning architecture is constructed, an improved encoder-decoder structure is used for generating a strategy vector, and the strategy vector is constructed. And performing hierarchical joint training through a multi-agent execution network, inputting a real-time state and a strategy vector into the trained deep reinforcement learning architecture, generating a scheduling instruction, and performing hierarchical model prediction control in combination with a Bayesian neural network ensemble model and a recurrent neural network prediction model to realize optimized scheduling of chip production logistics.

The invention discloses a method and a system for collecting decision data by an intelligent agent driven by a multi-modal large model. The method comprises the following steps: collecting original data of a predetermined sensor, calculating information entropy and a normalized mass score, and generating a fusion feature vector through self-adaptive multi-modal information fusion; time sequence sensitive multi-mode alignment processing is carried out on the image, and time sequence alignment features are obtained by combining timestamp embedding and a self-attention mechanism; based on this, decision distribution is generated by using a Bayesian neural network, uncertainty is evaluated, and a final decision and index set is generated; evaluating a task score in combination with task description, and making an execution plan according to a resource state; initializing the model, generating synthetic data, optimizing the model and storing experience. According to the invention, the efficient decision-making process of the intelligent trolley is realized, and the execution capability and task completion precision of the intelligent trolley are improved.

The invention discloses a dynamic weight correction and path deviation probability prediction method for a vehicle track, which comprises the following steps of: acquiring vehicle data and multi-source dynamic data of the vehicle track in real time through an optical sensor, a radio wave sensor and an inertial navigation sensor, carrying out space-time calibration, extracting obstacle characteristics and road structure characteristics, and predicting the path deviation probability of the vehicle track. The obstacle movement trend is quickly captured through a space-time diagram sequence and a diagram convolutional neural network, real-time obstacle avoidance is realized in combination with dynamic weight adjustment, a driving intention is predicted by using a Bayesian neural network, a trajectory planning strategy is adjusted through weight correction, and uncertainty is reduced by using multi-source data fusion and probabilistic prediction. A closed-loop feedback mechanism continuously optimizes the model, and efficient operation is kept in complex scenes such as intersections and roundabout through real-time weight adjustment and closed-loop feedback, so that the purpose of quickly responding to dynamic obstacles or driving behavior changes can be achieved, and the precision of the path deviation prediction probability is improved.

The invention discloses a power plant equipment fault prediction method based on a time sequence large model, and the method comprises the following steps: S1, collecting and preprocessing the time sequence data of a multi-source sensor of a power plant, and generating a standardized time sequence data set; s2, constructing a time sequence large model, inputting standardized data, and outputting a future operation state predicted value; s3, comparing the running state prediction value with an actual measurement value to generate a prediction residual sequence; s4, constructing a Bayesian neural network model, inputting a prediction residual sequence, and outputting error probability distribution; s5, optimizing a Bayesian neural network structure and hyper-parameters by adopting an ant colony optimization algorithm; s6, confidence interval estimation is executed, and whether the state is a high-risk state or not is judged; and S7, outputting a running state label, and dynamically acquiring a data closed-loop updating model. According to the invention, high-precision prediction and uncertainty evaluation of the operation state of the power plant equipment are realized, so that the accuracy and response time efficiency of fault early warning are improved.

The invention relates to the crossing field of material design and artificial intelligence, in particular to a solid electrolyte intelligent inverse design method fusing a graph neural network and confidence analysis. According to the method, a prediction framework integrating multiple models is constructed, support vector regression, gradient boosting regression, a deep neural network and a graph neural network are included, component, process and structure parameter characteristics are fully fused, and the nonlinear mapping relation between input variables and performance parameters such as resistivity and conductivity is efficiently learned. In order to improve the credibility, a Bayesian neural network and a Monte Carlo method are further introduced, a confidence interval corresponding to each group of prediction results is output, and quantitative evaluation of the credibility of the prediction value is realized. In the inverse design module, high-dimensional submerged space parameters are generated based on a variational auto-encoder, and intelligent recommendation of parameter combination driven by target performance is realized in combination with strategies such as Bayesian optimization and a genetic algorithm. The design efficiency of the solid electrolyte and the success rate of material discovery can be effectively improved.

The invention relates to the technical field of simulation model optimization, and discloses an aero-engine model Bayesian optimization method for quantizing uncertainty, and the method comprises the steps: building a probability mapping relation from a component index to an output response through constructing a Bayesian neural network agent model based on a probability weight coefficient; and by taking the difference between the output response and the corresponding complete machine test data as a multi-objective loss function and taking the minimization of the multi-objective loss function as an optimization objective, optimizing the component indexes by adopting a Bayesian optimization method based on a Gaussian process to obtain an optimal component index combination. Not only is a nonlinear relationship between high-dimensional parameters and simulation-test deviation accurately modeled through a neural network, but also efficient search of a parameter space is realized through a Gaussian process. The technical problems that when a traditional optimization method is used for processing the high-dimensional, strong-nonlinearity and multi-parameter coupling complex optimization problem of the aero-engine, the calculation efficiency is low, local optimum is prone to occurring, and result uncertainty cannot be quantified are solved.

The invention discloses an urban water supply management data trend analysis method based on space-time analysis, and relates to the field of data processing, and the method comprises the steps: collecting data in real time through an urban water supply pipe network sensor network, building a space-time unified coordinate system, building a space-time Kriging interpolation model based on pipe network topology, and achieving the space-time alignment of multi-source data; dividing an adaptive space-time grid by using a Voronoi diagram and a sliding window mechanism, and calculating multi-dimensional features; constructing a dynamic space-time diagram by taking a grid as a node, performing multi-step prediction in combination with a space-time diagram convolution circulation network, fusing a Kriging interpolation result, and evaluating an abnormal probability and a confidence interval through a Bayesian neural network; a monitoring layer, a prediction layer and a risk layer are overlaid in a three-dimensional GIS, a dynamic thermodynamic diagram is generated, an early warning path is optimized based on a Dijkstra algorithm, and a minimum risk topology path is output. The method has the advantages that through space-time analysis and accurate prediction, the intelligence, stability and emergency response efficiency of urban water supply management are remarkably improved, and powerful support is provided for smart city construction.

The invention discloses a Bayesian neural network-based concrete durability prediction method, which is characterized in that durability prediction is carried out by designing a multilayer Bayesian neural network model and combining data such as a concrete mix proportion, external environment conditions and material attributes. The Bayesian neural network updates the weight and bias through random sampling, and can quantify the uncertainty of prediction and provide a prediction confidence interval. The method comprises the specific steps of data preprocessing, network training, sampling, calculating and outputting a mean value and a standard deviation, optimizing model parameters through a gradient descent method, and finally evaluating model prediction performance. According to the method, the uncertainty in concrete durability prediction can be effectively quantified, more scientific and accurate decision support is provided for building engineers, and the cost and time of experimental testing are reduced.

The invention relates to the technical field of artificial intelligence, in particular to a multi-modal emotion recognition and interaction adjusting system and method based on uncertainty evaluation, and solves the defects of uncertainty processing, robustness of interaction strategies, complementary information mining degree among modals and the like in the man-machine interaction process in the prior art. Depth application finiteness is caused by lack of modeling for feature uncertainty after fusion. The uncertainty of an emotion recognition result is quantified through technologies such as multi-modal feature fusion and Bayesian neural network / model integration, an interaction strategy is dynamically adjusted according to an uncertainty score, emotion clarification or conservative response is triggered in a high-uncertainty scene, the robustness of human-computer interaction and the user experience are improved, and the user experience is improved. The method is suitable for intelligent customer service, government affair consultation, medical inquiry and other scenes with high requirements for emotion interaction accuracy.

The invention discloses an equipment prediction maintenance framework based on probability residual life, and belongs to the technical field of fault prediction and health management (PHM). According to the framework, through combining a Bayesian neural network and a reinforcement learning technology, probability prediction and dynamic maintenance decision optimization of the residual life of equipment are realized. The method specifically comprises the following steps: acquiring sensor data in equipment operation, and preprocessing to generate a training data set; constructing a Bayesian neural network (BNN), utilizing variation reasoning to approximate posteriori distribution, and outputting probability distribution of residual life through Monte Carlo sampling; based on a probability prediction result, constructing a reinforcement learning environment model, and defining a state space containing residual life distribution, a spare part state and a maintenance action and a reward function; a Double DQN algorithm is adopted to optimize a maintenance strategy, and intelligent decision-making of the optimal maintenance time and the optimal ordering time of equipment is dynamically realized through an epsilon-greedy algorithm, so that the maintenance cost and the fault risk are minimized. According to the method, probability residual life distribution and reinforcement learning decision are innovatively combined, the problem that a traditional point estimation model ignores uncertainty is solved, an intelligent maintenance framework is constructed through a reinforcement learning method, and dynamic optimization of maintenance decision is achieved. The NASA aero-engine data set verification shows that compared with a traditional method, the optimization effects of prediction errors, uncertainty quantification, the maintenance cost rate and the like are remarkable, and the reliability and economical efficiency of equipment are effectively balanced.

The invention discloses an ice lake feedback monitoring and early warning method based on artificial intelligence and multi-source data fusion, and belongs to the technical field of natural disaster monitoring, and the method specifically comprises the steps: extracting ice lake state feature vectors of multiple spatio-temporal data through cross-modal self-supervised learning, and constructing a three-dimensional virtual ice lake model; based on the feature vectors, constructing a glacier three-dimensional stress field model by using a finite element method, constructing a seepage channel graph model by using a graph neural network, and fusing the features of the glacier three-dimensional stress field model and the seepage channel graph model to form mechanical-seepage coupling state vectors; constructing a pulse neural network to simulate glacier fracture extension and seepage mutation pulse events; constructing a heterogeneous graph federated architecture and outputting a federated weight matrix in combination with a dynamic weighted aggregation strategy; the federal learning weight and the three-dimensional model output result are fused, the outburst posterior probability is calculated through a Bayesian neural network, and a fourth-level early warning signal is generated; according to the invention, the multi-dimensional characterization and coupling process simulation of the state of the ice lake is realized, and the early warning accuracy and timeliness are improved.

The invention provides a method for predicting the residual life of a power transformer based on digital-analog fusion, and belongs to the technical field of transformer detection.The method comprises the steps that multi-dimensional sensor data of the power transformer is collected, wavelet transform preprocessing is conducted, a normalized data matrix is established, a physical equation is established, and a deterministic physical model is formed; a data-driven model is established based on an improved adaptive multi-scale network to realize multi-scale feature adaptive extraction, a topological phase change algorithm is introduced to identify key transition points in an aging process, and a deterministic physical model and the data-driven model are fused to establish a digital-analog fusion prediction framework. A generative adversarial network is adopted to perform data enhancement to solve the problem of scarcity of fault samples, a Bayesian neural network and a Monte Carlo random inactivation technology are utilized to construct an uncertainty quantization framework to output a residual life prediction value and a confidence interval thereof, and the technical problem that the prediction precision of the residual life of the transformer is not high is solved.

The invention relates to the technical field of numerical control machining process optimization, in particular to an aluminum alloy milling parameter optimization method based on an improved Bayesian neural network, and the method comprises the following steps: S1, building an aluminum alloy thin-wall part milling finite element model based on a Johnson-Cook constitutive model; s2, carrying out a single-factor milling experiment, and verifying the reliability of the finite element model; s3, constructing a Bayesian neural network model fused with a multi-head attention mechanism, and establishing a mapping relation between process parameters and surface quality indexes; s4, establishing a multi-objective optimization model based on the MHA-BNN prediction model, and solving by adopting an NSGA-II algorithm; and S5, performing experimental verification and analysis on an optimization result. According to the method, a multi-head attention mechanism is introduced into a Bayesian neural network, and a milled surface roughness and residual stress probability prediction model is constructed; and a multi-objective optimization framework fusing physical constraint and data driving is established, collaborative optimization of the surface roughness, the residual stress and the processing efficiency is realized, and meanwhile, the problem that a traditional deep learning method is insufficient in prediction precision under a small sample condition is solved.

The invention relates to the technical field of design and application of two-dimensional materials, and discloses a design and simulation method of a two-dimensional semiconductor nanometer transistor, which comprises the following steps: collecting multi-source data of design and simulation of the two-dimensional semiconductor nanometer transistor, and screening through a data quality evaluation model to obtain a layered data structure; constructing and training a Bayesian neural network model; key influence parameters are identified through a multi-scale feature extraction algorithm and a parameter correlation analysis algorithm, and an attention algorithm is applied to pay attention to an important parameter combination; meanwhile, the uncertainty is predicted, a sampling point with the most information content is generated, and a prediction confidence interval is provided; through active learning feedback optimization circulation, high-value data points are selected based on prediction uncertainty for verification, and the model is updated until preset precision or resource constraint is achieved; according to the method, through multi-scale feature representation and parameter correlation analysis, invalid parameter exploration is reduced, and meanwhile, the model interpretability is improved through integrated physical constraint and uncertainty quantification.

The invention discloses a high-rise fire intelligent positioning voice alarm method and system, and particularly relates to the technical field of voice alarm, and the method comprises the steps: taking the concentration of different types of smog generated by combustion of different materials, the brightness value of flame, and the spreading speed information of flame as the input of a fuzzy Bayesian neural network model; taking the fire hazard grade probability distribution as the output of a fuzzy Bayesian neural network model, determining the fire hazard grade of the fire hazard area according to the fire hazard grade probability distribution, collecting the crowd flow information and the area geographical location information in the monitoring area, and carrying out the comprehensive analysis of the crowd flow information and the geographical location information in the monitoring area. According to the invention, the fire hazard monitoring efficiency and accuracy are improved, the specific condition of the fire hazard is combined with the alarm level, and the flexibility and effectiveness of the fire hazard emergency response are realized.

The invention relates to the field of intelligent early warning of guaranteed power supply, and provides an abnormal early warning method and system for a guaranteed power supply system, and the method comprises the steps: obtaining the topological structure, real-time operation data and equipment state data of each node in a guaranteed power supply network, employing the conditional entropy and instantaneous causal entropy calculation technology, precisely evaluating the causal intensity between variables, and achieving the early warning of the abnormal state of the guaranteed power supply system. And a causal adjacency matrix dynamically changing along with time is constructed to reflect a complex causal relationship in the network. Then, in combination with an equipment topological structure and dynamic causal information, multi-scale time sequence features are extracted through feature updating and expansion causal convolution, and a comprehensive feature vector is formed; after the vector is input into the Bayesian neural network, a plurality of prediction value samples are generated through sampling weight distribution, a mean value is used as a prediction result, and the prediction uncertainty is measured through variance. Accurate prediction and timely early warning of potential anomalies of the power supply system are realized, and the stability and safety of the system are effectively improved.

The invention belongs to the technical field of integrated circuits, and particularly relates to an analog circuit yield optimization method under multiple environment angles based on a Bayesian neural network. According to the method, a Bayesian neural network (BNN) is used for carrying out synchronous modeling on yield and nominal performance under multiple environment angles, and yield optimization under the multiple environment angles is executed by embedding the BNN into a Bayesian optimization framework. The BNN model provided by the invention can better learn the correlation between the yield and the performance under different environmental angles, provides a strong modeling capability for the yield and the uncertainty thereof, and improves the yield optimization efficiency. Compared with other baseline methods, under the condition that the same target yield is achieved, the circuit simulation time cost can be saved by 45.3%, under the same circuit simulation time cost, a design point with the higher yield can be obtained, and the yield is improved by 3.2%.

The invention provides a mechanical arm control method and system based on mechanism-Bayesian joint modeling, and relates to the technical field of robot control, and the method comprises the steps that firstly, a mechanical arm mechanism model is constructed, parameters of the mechanical arm mechanism model are estimated, and preliminary dynamics prediction is obtained; then, combining with motor driving torque observation data, establishing a random mathematical model of mechanism model residual errors, and decomposing the random mathematical model into deterministic and random parts; carrying out probability learning on the residual error by utilizing a Bayesian neural network, and outputting a prediction mean value and a variance of the residual error; in combination with preliminary dynamic prediction and residual information, constructing a data-driven uncertainty adaptive control law without dependence of an acceleration signal, and carrying out random stability analysis; and a stable joint driving torque instruction is generated, and high-precision trajectory tracking of the mechanical arm is achieved. According to the method, the interpretability of the mechanism model and the high adaptability of the data driving model are combined, the control precision and flexibility are effectively considered, and the robustness and reliability of the mechanical arm in the complex dynamic environment are improved.

The invention discloses a mechanical equipment residual service life prediction and uncertainty quantitative analysis method and device. The method comprises the following steps: acquiring multi-dimensional time sequence sensor data generated by mechanical equipment to be predicted in an operation process; inputting the multi-dimensional time sequence sensor data into a pre-trained physical constraint Bayesian neural network model; wherein the physical constraint Bayesian neural network model comprises a segmented bidirectional long short-term memory network feature extraction module, a hierarchical gating recursive regression network degradation dynamic modeling module and a Bayesian reasoning and physical constraint fusion regression module which are connected in sequence; the segmented bidirectional long-short-term memory network feature extraction module is used for processing input multi-dimensional time sequence sensor data and outputting hidden feature vectors representing local degradation dynamics of equipment; the hierarchical gating recursive regression network degradation dynamic modeling module is used for processing hidden feature vectors and capturing global degradation dynamic features through a complex numerical value hidden state updating mechanism; the Bayesian reasoning and physical constraint fusion regression module is used for carrying out Weibull distribution parameter regression based on the global degradation dynamic characteristics, introducing a deep implicit physical residual constraint and outputting a probability distribution parameter of the residual service life; and based on the probability distribution parameters, generating a residual service life prediction result and uncertainty quantitative information of the mechanical equipment.

The invention discloses a water pollution source tracking and atmospheric particulate distribution analysis method based on machine learning, comprising the following steps: S1, collecting and preprocessing multi-source data to generate an environment data set; s2, pollution diffusion characteristics are extracted, pollution sources are reasoned, and a water pollution tracking result is generated; s3, constructing a deep Bayesian neural network model, and outputting particulate matter concentration prediction and a confidence interval; s4, introducing a zebra optimization algorithm, and jointly optimizing a model structure and hyper-parameters; s5, fusing the water pollution tracking result and the environmental characteristics, and outputting a prediction result; s6, collecting real-time monitoring data, calculating a prediction residual error and feeding back the prediction residual error to the optimization model; and S7, generating a pollution thermodynamic diagram and a trend prediction diagram, and outputting a pollution level and an early warning suggestion. According to the invention, by fusing water pollution source tracking and atmospheric particulate distribution modeling, intelligent linkage analysis of pollution propagation path identification and air quality accurate prediction is realized.

The invention discloses a tire force estimation method based on in-tire sensing information and a Bayesian neural network. An acceleration type intelligent tire finite element model is built based on a finite element and coordinate system conversion theory; the method comprises the following steps of: determining an optimal mounting position of an acceleration sensor aiming at the three-dimensional force of the tire by adopting a Fourier amplitude sensitivity test (FAST) method, and determining the optimal mounting position of the acceleration sensor aiming at the three-dimensional force of the tire; the method comprises the following steps of: acquiring acceleration signal-tire force data sets under different test working conditions, extracting acceleration signals of a grounding imprinting area, and screening out optimal input characteristics by adopting a linear discriminant analysis (LDA) method; processing the acceleration signal-tire force data set after optimal feature screening by adopting a linear normalization theory; according to the characteristics of training data and test data, a tire force estimation algorithm based on a Bayesian neural network is established, and finally a predicted value and a predicted variance of tire force are output. The method is high in prediction precision, good in stability and high in generalization performance, and a more reliable technical scheme is provided for tire force estimation.

The invention provides a duration prediction method and device for a target time period and a medium, and relates to the technical field of data processing, and the method comprises the steps: building an initial duration prediction model based on a preset task feature tag list, a progressive hierarchical extraction framework and a mixed Bayesian neural network; training the initial time length prediction model based on a preset training data set, optimizing the initial time length prediction model based on a joint loss optimization method to obtain a target time length prediction model, and inputting a feature value list corresponding to the flight task of the to-be-predicted aircraft into the target time length prediction model to obtain a target prediction time length list, it can be known that the target time length prediction model provided by the invention combines the progressive hierarchical extraction framework and the mixed Bayesian neural network, has strong feature extraction capability, can simultaneously predict the time lengths of multiple target time periods, and is beneficial for improving the efficiency and accuracy of time length prediction.

The invention relates to a Bayesian KAN fused aircraft rapid multidisciplinary uncertainty analysis method, and belongs to the technical field of spacecraft manufacturing and application. According to the method, Bayesian learning is combined with a Colmogorov-Arnodel network, a Bayesian KAN neural network which can cope with high-dimensional input uncertainty, can quantify model cognitive uncertainty and is higher in fitting capacity is provided, a BKAN network agent model is constructed for a subsubject analysis model, time-consuming numerical simulation of each subsubject is replaced, and the calculation amount is reduced; with reference to a basic framework of a global sensitivity equation GSE of a deterministic field coupling multidisciplinary system, input random input and model cognition uncertainty are introduced, and expressions of mean values and covariances of disciplinary coupling variables and system responses are deduced on the GSE framework; a rapid semi-analytical multidisciplinary random and model cognition hybrid uncertainty propagation method is established, efficient multidisciplinary uncertainty analysis is achieved, and the problems that an existing method is poor in precision and large in calculation amount are solved.

The invention provides a model and data driving fused mechanical arm gravity compensation algorithm, and aims to solve the problem of poor mechanical arm gravity compensation precision caused by model uncertainty. According to the algorithm, firstly, a statics equation is established, secondly, multiple data are trained through a Bayesian neural network (BNN), a predicted torque and a standard are generated, then, through a Bayesian fusion method, a model calculation result and a neural network fitting result are subjected to Bayesian fusion according to a standard deviation, and a fusion estimation torque is generated. In order to solve the problem that the generalization ability of a data driving model is insufficient in an unseen scene, the algorithm uses a physical model as a constraint condition to limit the range of fusion torque, high deviation caused by insufficient model generalization is reduced, and then stability is ensured. Compared with a traditional modeling method, the fusion strategy is combined with the self-adaptability of physical constraint and data driving, the influence of modeling errors on the control precision is effectively reduced, and the precision and robustness of gravity compensation are remarkably improved.

The invention discloses a power load prediction method, and the method comprises the steps: firstly solving an extreme event data sparsity problem through a generative adversarial network, and constructing an event time sequence library through a time sequence anomaly detection algorithm; then analyzing the causal relationship between the event and the load by applying a causal discovery algorithm, and converting prediction output into probability distribution by adopting a Bayesian neural network to quantify uncertainty; constructing a prediction model triggered by an event, and generating a multi-time scale probability prediction interval; and finally, generating a multi-scene prediction result through Monte Carlo simulation, quantifying the system recovery capability in combination with a toughness index, and integrating the system recovery capability to a decision support system to generate a risk response scheme. According to the method, the accuracy and robustness of load prediction under the extreme climate are remarkably improved, full-chain risk insight from early warning to recovery is realized, and prospective decision support is provided for safe operation of a power system.

The invention relates to the technical field of oil and gas field development, in particular to a fracture parameter inversion method based on a Bayesian neural network, which comprises the following steps: establishing a bottom hole net pressure conversion model based on an actual construction curve, and drawing a bottom hole net pressure curve; calculating a bottom hole net pressure index sequence and a corresponding time sequence; establishing a shaft bottom crack extension mode judgment criterion on the basis of a classic double logarithmic curve analysis method; taking the net pressure index sequence and the time sequence obtained in the previous step as input data, combining actual physical parameter constraints, and establishing an inversion fracture parameter model based on a Bayesian neural network; and inputting the pressure index sequence to be inverted into the Bayesian neural network model to obtain a specific fracture parameter inversion result. According to the technical scheme, the confidence interval of the prediction result can be given, the prediction result and uncertainty quantification capability can be synchronously provided, and the reliability and decision support value of the inversion result are greatly improved.

The invention provides a radiation shielding multi-objective optimization design method based on a Bayesian neural network, and the method is characterized in that the method comprises the following steps: S1, obtaining sample data; s2, network construction and evaluation; s3, robust design optimization is carried out; according to the method, cognitive uncertainty in the modeling process of an agent model is quantified by using a loss function based on variational inference and KL divergence derivation, a nuclear radiation shielding calculation agent model considering uncertainty factors is obtained, and radiation shielding calculation is performed on an objective function in multi-objective optimization design through the constructed agent model. And a Pareto optimal solution set with certain robustness is obtained in combination with robust optimization design. According to the method, prior distribution is added to the weights of a neural network model, then the change conditions of the weights when data are given are obtained to measure the cognitive uncertainty in radiation shielding simulation, and the robustness of an optimization result is improved.

The invention provides an electric quantity prediction method and system fusing physical constraint factors, and relates to the technical field of electric quantity prediction. Historical load, weather, electricity price and calendar data are collected, and a key feature set is constructed through preprocessing and feature selection; a prediction model with the physical information neural network as the core is constructed, the prediction model comprises a recursion sub-module used for short-term prediction and a trend sub-module used for long-term prediction, and a physical constraint loss item based on a physical rule is introduced into model training so as to enhance the generalization ability; a multi-time granularity modeling framework is adopted, uncertainty quantization is achieved through a Monte Carlo Dropout or Bayesian neural network, and a confidence interval of a predicted value is output; and finally, causal reasoning is carried out through a Shapley value algorithm and anti-fact simulation, and key influence factors are identified. According to the method, the precision, stability and interpretability of electric quantity prediction are effectively improved, and reliable support is provided for power grid dispatching and decision making.

The invention discloses a number-real fusion test method for reliability of a blade disc structure of an aero-engine, and is applied to the field of time-varying reliability evaluation of the aero-engine. The method comprises the following steps: acquiring response data of a key part of a leaf disc structure under a full flight cycle; establishing a finite element virtual entity to obtain a time-varying input variable and a time-varying output response of the aero-engine bladed disc structure under the full flight cycle; establishing a physical information embedded loss function, and establishing an optimal physical information embedded multi-response regression model based on a Bayesian neural network inference framework; and extracting a large batch of time-invariant data sets, fitting a limit state function by using the established multi-response regression model, and calculating the reliability of the time-varying system based on the Monte Carlo thought to complete the reliability evaluation of the time-varying system. According to the method, a complex time-varying reliability evaluation problem is converted into a time-invariant response regression problem, and the calculation precision is effectively improved while the calculation task is simplified.

The invention discloses a self-adaptive parameter identification method for an SST turbulence model in shock wave boundary layer interference, and the method comprises the steps: S1, constructing a high-dimensional sample space according to a closed parameter interval range of the SST turbulence model under a determined working condition, carrying out the optimal Latin hypercube sampling, and uniformly obtaining the sample information in the whole space, and enabling the sample information to serve as an input parameter sample; s2, performing numerical simulation based on an input parameter sample and a determined working condition to form a model prior sample set; s3, according to the model prior sample set, constructing a flow field wall surface pressure prediction model under shock wave boundary layer interference based on an improved Bayesian neural network; and S4, based on the flow field wall surface pressure prediction model under the interference of the shock wave boundary layer, performing adaptive parameter identification on the SST turbulence model by adopting a particle swarm optimization algorithm to obtain the optimal closing parameter of the SST turbulence model for realizing the high-precision simulation of the flow field. According to the method, the numerical simulation precision and speed are improved, and technical support is provided for safe and reliable work of the scramjet engine in a complex environment.